Full-Text Search:
Home|About CNKI|User Service|中文
Add to Favorite Get Latest Update

The maximum likelihood identification method applied to insect morphometric data

Jean-Pierre Dujardin;Sebastien Dujardin;Dramane Kaba;Soledad Santillán-Guayasamín;Anita G.Villacís;Sitha Piyaselakul;Suchada Sumruayphol;Yudthana Samung;Ronald Morales Vargas;IRD,UMR 177 IRD-CIRAD INTERTRYP,Campus international de Baillarguet;école des Technologies Numériques Appliquées;Institut Pierre Richet,Institut National de Santé Publique;Center for Research on Health in Latin America,School of Biological Sciences,Pontifical Catholic University of Ecuador;Department of Anatomy,Faculty of Medicine Siriraj Hospital,Mahidol University;Department of Medical Entomology,Faculty of Tropical Medicine,Mahidol University;  
To distinguish species or populations using morphometric data is generally processed through multivariate analyses,in particular the discriminant analysis.We explored another approach based on the maximum likelihood method.Simple statistics based on the assumption of normal distribution at a single variable allows to compute the chance of observing a particular data(or sample) in a given reference group.When data are described by more than one variable,the maximum likelihood(MLi) approach allows to combine these chances to find the best fit for the data.Such approach assumes independence between variables.The assumptions of normal distribution of variables and independence between them are frequently not met in morphometrics,but improvements may be obtained after some mathematical transformations.Provided there is strict anatomical correspondence of variables between unknown and reference data,the MLi classification produces consistent classification.We explored this approach using various input data,and compared validated classification scores with the ones obtained after the Mahalanobis distance-based classification.The simplicity of the method,its fast computation,performance and versatility,make it an interesting complement to other classification techniques.
Download(CAJ format) Download(PDF format)
CAJViewer7.0 supports all the CNKI file formats; AdobeReader only supports the PDF format.
©CNKI All Rights Reserved